Motion field texture synthesis

Directable Motion Texture Synthesis

We present an algorithm for creating animation by keyframing, stylistic sequencing, random sampling, or by a combination of these methods. We first “learn” a motion model from existing animation data (such as motion capture), and optionally label specific frames as corresponding to specific “styles.” The system can then generate a new motion in the style of the training data, but matching the p...

Dynamic Texture Synthesis Using Motion Distribution Statistics

In this paper we propose a motion based approach for synthesizing dynamic textures. Dynamic textures are natural phenomenon characterized by their distinctive motion patterns. Synthesis of these textures is thus considered as the regeneration of a motion pattern that has identical motion distribution of a source texture. In this paper we propose a synthesis technique where new textures are gene...

Steerable texture synthesis for vector field visualization

Vector field visualization using Markov Random Field texture synthesis

Vector field visualization generates an image to convey the information existing in the data. We use Markov Random Field texture synthesis methods to generate the visualization from a set of example textures. The examples textures are chosen according to the vector data for each pixel of the output. This leads to dense visualizations with arbitrary example textures.

Motion Texture

Motion capture data is often used in movies and video games because it is able to realistically depict human motion. One of its greatest limitations is the inflexibility for reuse in different projects. To get the exact shot that you would like requires an individual motion capture session. This is not only costly but time consuming as well. We would like to be able to easily take existing data...